import shlex import subprocess subprocess.run( shlex.split( "pip install https://pkgs.dev.azure.com/onnxruntime/2a773b67-e88b-4c7f-9fc0-87d31fea8ef2/_packaging/7fa31e42-5da1-4e84-a664-f2b4129c7d45/pypi/download/onnxruntime-gpu/1.17/onnxruntime_gpu-1.17.0-cp310-cp310-manylinux_2_28_x86_64.whl --force-reinstall --no-deps" ) ) subprocess.run( shlex.split( "pip install package/nvdiffrast-0.3.1.torch-cp310-cp310-linux_x86_64.whl" ) ) if __name__ == "__main__": import os import sys sys.path.append(os.curdir) import torch torch.set_float32_matmul_precision('medium') torch.backends.cuda.matmul.allow_tf32 = True torch.set_grad_enabled(False) import fire import gradio as gr from gradio_app.gradio_3dgen import create_ui as create_3d_ui from gradio_app.all_models import model_zoo _TITLE = '''Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image''' _DESCRIPTION = ''' # [Project page](https://wukailu.github.io/Unique3D/) * High-fidelity and diverse textured meshes generated by Unique3D from single-view images. * The demo is still under construction, and more features are expected to be implemented soon. # NOTE: The Hugging Face demo is still under development and cannot produce any accurate results at the moment. ''' def launch(): model_zoo.init_models() with gr.Blocks( title=_TITLE, theme=gr.themes.Monochrome(), ) as demo: with gr.Row(): with gr.Column(scale=1): gr.Markdown('# ' + _TITLE) gr.Markdown(_DESCRIPTION) create_3d_ui("wkl") demo.queue().launch(share=True) if __name__ == '__main__': fire.Fire(launch)